In [1]:
#Deep Q Network for ENO
#Resetting the battery to BOPT on each day during training
#Increase no. of actions to 10
In [2]:
%matplotlib inline
In [3]:
import pulp
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

import pandas as pd
import numpy as np
from random import shuffle
from mpl_toolkits.mplot3d import Axes3D

import torch
import torch.nn as nn
import torch.nn.functional as F
In [4]:
np.random.seed(230228)
In [5]:
# Hyper Parameters
BATCH_SIZE = 24
LR = 0.01                   # learning rate
EPSILON = 0.9               # greedy policy
GAMMA = 0.9                 # reward discount
LAMBDA = 0.8                # parameter decay
TARGET_REPLACE_ITER = 24*7    # target update frequency (every week)
MEMORY_CAPACITY = 24*7*4      # store upto one month worth of memory   

N_ACTIONS = 10 #no. of duty cycles (0,1,2,3,4)
N_STATES = 4 #number of state space parameter [batt, enp, henergy, fcast]
HIDDEN_LAYER = 30
In [6]:
class ENO(object):
    def __init__(self, year=2010):
        self.year = year
        self.day = None
        self.hr = None
        
        self.TIME_STEPS = None
        self.NO_OF_DAYS = None
        
        self.BMIN = 0.0
        self.BMAX = 20000.0 #Battery capacity
        self.BOPT = 0.6 * self.BMAX #Assuming 60% of battery is the optimal level
        self.HMAX = 1000
        
        self.senergy = None #matrix with harvested energy data for the entire year
        self.fforecast = None #matrix with forecast values for each day
        
        self.batt = None #battery variable
        self.enp = None #enp at end of hr
        self.henergy = None #harvested energy variable
        self.fcast = None #forecast variable
    
    #function to map total day energy into day_state
    def get_day_state(self,tot_day_energy):
        if (tot_day_energy < 2500):
            day_state = 0
        elif (2500 <= tot_day_energy < 5000):
            day_state = 1
        elif (5000 <= tot_day_energy < 8000):
            day_state = 2
        elif (8000 <= tot_day_energy < 10000):
            day_state = 3
        elif (10000 <= tot_day_energy < 12000):
            day_state = 4
        else:
            day_state = 5
        return int(day_state)

    #function to get the solar data for the given year and prep it
    def get_data(self):
        filename = str(self.year)+'.csv'
        #skiprows=4 to remove unnecessary title texts
        #usecols=4 to read only the Global Solar Radiation (GSR) values
        solar_radiation = pd.read_csv(filename, skiprows=4, encoding='shift_jisx0213', usecols=[4])
        
        #convert dataframe to numpy array
        solar_radiation = solar_radiation.values
        solar_energy = np.array([i *0.0165*1000000*0.15*1000/(60*60) for i in solar_radiation])
        
        #reshape solar_energy into no_of_daysx24 array
        _senergy = solar_energy.reshape(-1,24)
        _senergy[np.isnan(_senergy)] = 0 #convert missing data in CSV files to zero
        self.senergy = _senergy
        
        
        #create a perfect forecaster
        tot_day_energy = np.sum(_senergy, axis=1) #contains total energy harvested on each day
        get_day_state = np.vectorize(self.get_day_state)
        self.fforecast = get_day_state(tot_day_energy)
        
        return 0
    
    def reset(self):
        
        self.get_data() #first get data for the given year
        
        self.TIME_STEPS = self.senergy.shape[1]
        self.NO_OF_DAYS = self.senergy.shape[0]
        
        print("Environment is RESET")
        
        self.day = 0
        self.hr = 0
        
        self.batt = self.BOPT #battery returns to optimal level
        self.enp = self.BOPT - self.batt #enp is reset to zero
        self.henergy = self.senergy[self.day][self.hr] 
        self.fcast = self.fforecast[self.day]
        
        state = [self.batt/self.BMAX, self.enp/(self.BMAX/2), self.henergy/self.HMAX, self.fcast/5] #normalizing all state values within [0,1] interval
        reward = 0
        done = False
        info = "RESET"
        return [state, reward, done, info]
    
    
    #reward function
    def rewardfn(self):
        mu = 0
        sig = 1000
#         return ((1./(np.sqrt(2.*np.pi)*sig)*np.exp(-np.power((self.enp - mu)/sig, 2.)/2)) * 2000000)-400


        if(np.abs(self.enp) <= 2400): #24hr * 100mW/hr
            return ((1./(np.sqrt(2.*np.pi)*sig)*np.exp(-np.power((self.enp - mu)/sig, 2.)/2)) * 1000000)
        else:
            return -100 - 0.05*np.abs(self.enp)
    
    def step(self, action):
        done = False
        info = "OK"
#         print("Next STEP")
        
        reward = 0
        e_consumed = (action+1)*50
        
        self.batt += (self.henergy - e_consumed)
        self.batt = np.clip(self.batt, self.BMIN, self.BMAX)
        self.enp = self.BOPT - self.batt
        
        if(self.hr < self.TIME_STEPS - 1):
            self.hr += 1
            self.henergy = self.senergy[self.day][self.hr] 
        else:
            if(self.day < self.NO_OF_DAYS -1):
                reward = self.rewardfn() #give reward only at the end of the day
                self.hr = 0
                self.day += 1
                self.henergy = self.senergy[self.day][self.hr] 
                self.fcast = self.fforecast[self.day]
            else:
                reward = self.rewardfn()
                done = True
                info = "End of the year"
                
        _state = [self.batt/self.BMAX, self.enp/(self.BMAX/2), self.henergy/self.HMAX, self.fcast/5]
        return [_state, reward, done, info]
In [7]:
class Net(nn.Module):
    def __init__(self, ):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(N_STATES, HIDDEN_LAYER)
        self.fc1.weight.data.normal_(0, 0.1)   # initialization
        
        self.fc2 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)
        self.fc2.weight.data.normal_(0, 0.1)   # initialization
        
        self.fc3 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)
        self.fc3.weight.data.normal_(0, 0.1)   # initialization
        
        self.fc4 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)
        self.fc4.weight.data.normal_(0, 0.1)   # initialization
        
        self.out = nn.Linear(HIDDEN_LAYER, N_ACTIONS)
        self.out.weight.data.normal_(0, 0.1)   # initialization

    def forward(self, x):
        x = self.fc1(x)
        x = F.relu(x)
        actions_value = self.out(x)
        return actions_value
In [8]:
class DQN(object):
    def __init__(self):
        self.eval_net, self.target_net = Net(), Net()
        print("Neural net")
        print(self.eval_net)

        self.learn_step_counter = 0                                     # for target updating
        self.memory_counter = 0                                         # for storing memory
        self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))     # initialize memory [mem: ([s], a, r, [s_]) ]
        self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)
        self.loss_func = nn.MSELoss()

    def choose_action(self, x):
        x = torch.unsqueeze(torch.FloatTensor(x), 0)
        # input only one sample
        if np.random.uniform() < EPSILON:   # greedy
            actions_value = self.eval_net.forward(x)
            action = torch.max(actions_value, 1)[1].data.numpy()
            action = action[0] # return the argmax index
        else:   # random
            action = np.random.randint(0, N_ACTIONS)
            action = action
        return action
    
    def choose_greedy_action(self, x):
        x = torch.unsqueeze(torch.FloatTensor(x), 0)
        # input only one sample
    
        actions_value = self.eval_net.forward(x)
        action = torch.max(actions_value, 1)[1].data.numpy()
        action = action[0] # return the argmax index

        return action

    def store_transition(self, s, a, r, s_):
        transition = np.hstack((s, [a, r], s_))
        # replace the old memory with new memory
        index = self.memory_counter % MEMORY_CAPACITY
        self.memory[index, :] = transition
        self.memory_counter += 1

    def learn(self):
        # target parameter update
        if self.learn_step_counter % TARGET_REPLACE_ITER == 0:
            self.target_net.load_state_dict(self.eval_net.state_dict())
        self.learn_step_counter += 1

        # sample batch transitions
        sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)
        b_memory = self.memory[sample_index, :]
        b_s = torch.FloatTensor(b_memory[:, :N_STATES])
        b_a = torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int))
        b_r = torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2])
        b_s_ = torch.FloatTensor(b_memory[:, -N_STATES:])

        # q_eval w.r.t the action in experience
        q_eval = self.eval_net(b_s).gather(1, b_a)  # shape (batch, 1)
        q_next = self.target_net(b_s_).detach()     # detach from graph, don't backpropagate
        q_target = b_r + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1)   # shape (batch, 1)
        loss = self.loss_func(q_eval, q_target)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
In [9]:
dqn = DQN()
eno = ENO(2010)
NO_OF_ITERATIONS = 30
avg_reward_rec = np.empty(1)
for iteration in range(NO_OF_ITERATIONS):
    print('\nCollecting experience... Iteration:', iteration)
    s, r, done, info = eno.reset()
    record = np.empty(4)

    while True:
    #     print([eno.day, eno.hr])

        a = dqn.choose_action(s)
    #     print("Actin is ",a)
        #state = [batt, enp, henergy, fcast]
        record = np.vstack((record, [s[0],s[2],r, a])) #record battery, henergy, reward and action
    #     print("Action is" , a)
        # take action
        s_, r, done, info = eno.step(a)
    #     print([s_,r])
    #     print("\n")
        if eno.hr == 0:
            eno.batt = eno.BOPT #resetting the battery to the optimal value for each day
        dqn.store_transition(s, a, r, s_)

        if dqn.memory_counter > MEMORY_CAPACITY:
            dqn.learn()

        if done:
            print("End of Data")
            break

        s = s_

    record = np.delete(record, 0, 0) #remove the first row which is garbage

    reward_rec = record[:,2]
    reward_rec = reward_rec[reward_rec != 0]
    print("Average reward =", np.mean(reward_rec) )
    avg_reward_rec = np.append(avg_reward_rec, np.mean(reward_rec))

    action_rec = record[:,3]

    fig = plt.figure(figsize=(10,5))

    ax1 = fig.add_subplot(1,2,1)
    ax1.plot(reward_rec,'y')
    plt.ylabel("REWARD")
    plt.xlabel("Day")
    ax1.set_ylim([-400,400])

    ax2 = fig.add_subplot(1,2,2)
    plt.hist(action_rec, rwidth=0.75)#     plt.ylabel("Action")

    fig.tight_layout()
    plt.show()

avg_reward_rec = np.delete(avg_reward_rec, 0, 0) #remove the first row which is garbage
plt.plot(avg_reward_rec,'b')
Neural net
Net(
  (fc1): Linear(in_features=4, out_features=30, bias=True)
  (fc2): Linear(in_features=30, out_features=30, bias=True)
  (fc3): Linear(in_features=30, out_features=30, bias=True)
  (fc4): Linear(in_features=30, out_features=30, bias=True)
  (out): Linear(in_features=30, out_features=10, bias=True)
)

Collecting experience...
Environment is RESET
End of Data
Average reward = -124.9330427304717
Collecting experience...
Environment is RESET
End of Data
Average reward = -43.65893871178736
Collecting experience...
Environment is RESET
End of Data
Average reward = -20.97206153726197
Collecting experience...
Environment is RESET
End of Data
Average reward = -6.539629383438981
Collecting experience...
Environment is RESET
End of Data
Average reward = -17.38190552566726
Collecting experience...
Environment is RESET
End of Data
Average reward = -26.710307550823043
Collecting experience...
Environment is RESET
End of Data
Average reward = -58.42041383389939
Collecting experience...
Environment is RESET
End of Data
Average reward = -28.27788939524282
Collecting experience...
Environment is RESET
End of Data
Average reward = -74.38424315528513
Collecting experience...
Environment is RESET
End of Data
Average reward = -37.52640402535007
Collecting experience...
Environment is RESET
End of Data
Average reward = -29.10957941490192
Collecting experience...
Environment is RESET
End of Data
Average reward = -63.32895597776135
Collecting experience...
Environment is RESET
End of Data
Average reward = -102.47510973670815
Collecting experience...
Environment is RESET
End of Data
Average reward = -45.847884983666695
Collecting experience...
Environment is RESET
End of Data
Average reward = -21.27204906749013
Collecting experience...
Environment is RESET
End of Data
Average reward = -0.1693766327262083
Collecting experience...
Environment is RESET
End of Data
Average reward = -45.934412257253676
Collecting experience...
Environment is RESET
End of Data
Average reward = -38.24189948915741
Collecting experience...
Environment is RESET
End of Data
Average reward = -47.96578436089214
Collecting experience...
Environment is RESET
End of Data
Average reward = -37.06190649338856
Collecting experience...
Environment is RESET
End of Data
Average reward = -59.569952720174435
Collecting experience...
Environment is RESET
End of Data
Average reward = -77.09543607206209
Collecting experience...
Environment is RESET
End of Data
Average reward = -43.39204955377233
Collecting experience...
Environment is RESET
End of Data
Average reward = -39.657357092743574
Collecting experience...
Environment is RESET
End of Data
Average reward = -91.6117214356516
Collecting experience...
Environment is RESET
End of Data
Average reward = -61.32310092251494
Collecting experience...
Environment is RESET
End of Data
Average reward = -47.750817877177234
Collecting experience...
Environment is RESET
End of Data
Average reward = -29.662449665578336
Collecting experience...
Environment is RESET
End of Data
Average reward = -44.35221704345237
Collecting experience...
Environment is RESET
End of Data
Average reward = -85.09158198352904
Out[9]:
[<matplotlib.lines.Line2D at 0x7f6a657079e8>]
In [10]:
print('\nTesting...')
s, r, done, info = eno.reset()
test_record = np.empty(4)

while True:
#     print([eno.day, eno.hr])

    a = dqn.choose_greedy_action(s)
    
    #state = [batt, enp, henergy, fcast]
    test_record = np.vstack((test_record, [s[0],s[2],r, a])) #record battery, henergy, reward and action
#     print("Action is" , a)
    # take action
    s_, r, done, info = eno.step(a)
#     print([s_,r])
#     print("\n")
    if eno.hr == 0:
        eno.batt = eno.BOPT #resetting the battery to the optimal value for each day
   
    if done:
        print("End of Data")
        break
       
    s = s_
Testing...
Environment is RESET
End of Data
In [11]:
test_reward_rec = test_record[:,2]
test_reward_rec = test_reward_rec[test_reward_rec != 0]
plt.plot(test_reward_rec)
Out[11]:
[<matplotlib.lines.Line2D at 0x7f6a615cf550>]
In [12]:
plt.plot(test_record[:,0],'r')
Out[12]:
[<matplotlib.lines.Line2D at 0x7f6a6170d978>]
In [13]:
#Average Battery Percentage
np.mean(test_record[:,0])
Out[13]:
0.7036326439618763
In [14]:
for DAY in range(eno.NO_OF_DAYS):
    START = DAY*24
    END = START+24

    fig = plt.figure(figsize=(10,4))
    st = fig.suptitle("DAY %s" %(DAY))

    ax1 = fig.add_subplot(141)
    ax1.plot(test_record[START:END,0])
    ax1.set_title("Battery")
    ax1.set_ylim([0,1])

    ax2 = fig.add_subplot(142)
    ax2.plot(test_record[START:END,1])
    ax2.set_title("Harvested Energy")
    ax2.set_ylim([0,1])

    ax3 = fig.add_subplot(144)
    ax3.axis('off')
    if END < (eno.NO_OF_DAYS*eno.TIME_STEPS):
        plt.text(0.5, 0.5, "REWARD = %.2f\n" %(test_record[END+1,2]),fontsize=14, ha='center')

    ax4 = fig.add_subplot(143)
    ax4.plot(test_record[START:END,3])
    ax4.set_title("Action")
    ax4.set_ylim([0,N_ACTIONS])

    fig.tight_layout()
    st.set_y(0.95)
    fig.subplots_adjust(top=0.75)
    plt.show()

KeyboardInterruptTraceback (most recent call last)
<ipython-input-14-1f594b071564> in <module>()
     26     ax4.set_ylim([0,N_ACTIONS])
     27 
---> 28     fig.tight_layout()
     29     st.set_y(0.95)
     30     fig.subplots_adjust(top=0.75)

/usr/local/lib/python3.6/dist-packages/matplotlib/figure.py in tight_layout(self, renderer, pad, h_pad, w_pad, rect)
   2273         kwargs = get_tight_layout_figure(
   2274             self, self.axes, subplotspec_list, renderer,
-> 2275             pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)
   2276         self.subplots_adjust(**kwargs)
   2277 

/usr/local/lib/python3.6/dist-packages/matplotlib/tight_layout.py in get_tight_layout_figure(fig, axes_list, subplotspec_list, renderer, pad, h_pad, w_pad, rect)
    326                                      subplot_list=subplot_list,
    327                                      ax_bbox_list=ax_bbox_list,
--> 328                                      pad=pad, h_pad=h_pad, w_pad=w_pad)
    329 
    330     if rect is not None:

/usr/local/lib/python3.6/dist-packages/matplotlib/tight_layout.py in auto_adjust_subplotpars(fig, renderer, nrows_ncols, num1num2_list, subplot_list, ax_bbox_list, pad, h_pad, w_pad, rect)
    112             continue
    113 
--> 114         tight_bbox_raw = union([ax.get_tightbbox(renderer) for ax in subplots
    115                                 if ax.get_visible()])
    116         tight_bbox = TransformedBbox(tight_bbox_raw,

/usr/local/lib/python3.6/dist-packages/matplotlib/tight_layout.py in <listcomp>(.0)
    113 
    114         tight_bbox_raw = union([ax.get_tightbbox(renderer) for ax in subplots
--> 115                                 if ax.get_visible()])
    116         tight_bbox = TransformedBbox(tight_bbox_raw,
    117                                      fig.transFigure.inverted())

/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py in get_tightbbox(self, renderer, call_axes_locator)
   4168             bb.append(self._right_title.get_window_extent(renderer))
   4169 
-> 4170         bb_xaxis = self.xaxis.get_tightbbox(renderer)
   4171         if bb_xaxis:
   4172             bb.append(bb_xaxis)

/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py in get_tightbbox(self, renderer)
   1145         ticks_to_draw = self._update_ticks(renderer)
   1146 
-> 1147         self._update_label_position(renderer)
   1148 
   1149         # go back to just this axis's tick labels

/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py in _update_label_position(self, renderer)
   1904         # get bounding boxes for this axis and any siblings
   1905         # that have been set by `fig.align_xlabels()`
-> 1906         bboxes, bboxes2 = self._get_tick_boxes_siblings(renderer=renderer)
   1907 
   1908         x, y = self.label.get_position()

/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py in _get_tick_boxes_siblings(self, renderer)
   1889         for nn, axx in enumerate(grp.get_siblings(self.axes)):
   1890             ticks_to_draw = axx.xaxis._update_ticks(renderer)
-> 1891             tlb, tlb2 = axx.xaxis._get_tick_bboxes(ticks_to_draw, renderer)
   1892             bboxes.extend(tlb)
   1893             bboxes2.extend(tlb2)

/usr/local/lib/python3.6/dist-packages/matplotlib/axis.py in _get_tick_bboxes(self, ticks, renderer)
   1128         for tick in ticks:
   1129             if tick.label1On and tick.label1.get_visible():
-> 1130                 extent = tick.label1.get_window_extent(renderer)
   1131                 ticklabelBoxes.append(extent)
   1132             if tick.label2On and tick.label2.get_visible():

/usr/local/lib/python3.6/dist-packages/matplotlib/text.py in get_window_extent(self, renderer, dpi)
    920             raise RuntimeError('Cannot get window extent w/o renderer')
    921 
--> 922         bbox, info, descent = self._get_layout(self._renderer)
    923         x, y = self.get_unitless_position()
    924         x, y = self.get_transform().transform_point((x, y))

/usr/local/lib/python3.6/dist-packages/matplotlib/text.py in _get_layout(self, renderer)
    303         baseline = 0
    304         for i, line in enumerate(lines):
--> 305             clean_line, ismath = self.is_math_text(line, self.get_usetex())
    306             if clean_line:
    307                 w, h, d = renderer.get_text_width_height_descent(clean_line,

/usr/local/lib/python3.6/dist-packages/matplotlib/text.py in is_math_text(s, usetex)
   1189             return s, True
   1190         else:
-> 1191             return s.replace(r'\$', '$'), False
   1192 
   1193     def set_fontproperties(self, fp):

KeyboardInterrupt: